September 13, 2018
Professor; Director of Affective Computing Research; Faculty Chair, MIT Mind+Hand+Heart; MIT Media Lab
Talk abstract: Years ago, our team at MIT created wearable as well as non-contact imaging technology and machine learning algorithms to detect changes in human emotion. As we shrunk the sensors and made them able to comfortably collect data 24/7, we started to discover several surprising findings, such as that autonomic activity measured through a sweat response was more specific than 100 years of studies had assumed. While we originally thought this signal of “arousal” or “stress” was quite generally related to overall activation, we learned it could peak even when a patient’s EEG showed a lack of cortical brain activity. This talk will highlight some of the most surprising findings along the journey of measuring emotion “in the wild”with implications for anxiety, depression, sleep-memory consolidation, epilepsy, autism, pain studies, and more. What is the grand challenge we aim to solve next?
For more information about Prof. Picard: http://web.media.mit.edu/~picard/